Learn to deploy a pre-trained BERT model for sentiment analysis as a REST API using FastAPI and Uvicorn in this comprehensive tutorial. Explore the process of initializing a new project with pyenv and pipenv, creating a stubbed API response, and integrating the BERT model into your project. Discover how to handle real-world scenarios, such as reacting to seismic events, and seamlessly incorporate model predictions into your REST API. Gain practical insights into machine learning deployment, natural language processing, and transfer learning using PyTorch, Hugging Face transformers, and FastAPI.
Deploy with FastAPI - Sentiment Analysis with BERT Using Huggingface, PyTorch, and Python Tutorial
Overview
Syllabus
Demo of the REST API
Initialize a new project with pyenv and pipenv
Create a stubbed API response
Add the pre-trained BERT model to our project
19:35 Reacting to a 4.5 on the Richter scale earthquake
Integrate our model predictions in the REST API
Taught by
Venelin Valkov
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